Zurich is one of Europe’s hotbeds for fintech innovation. Here are three companies using digitalization tools to address different parts of the asset-management industry.
This company, created by Andreas Zimmerman, is attempting to build the next generation in robo advisory. If Robo 1.0 was limited by its inability to account for a client’s holistic wealth, assets, and needs, then Robo 2.0 has to deliver a far more comprehensive service.
Zimmerman, formerly a partner at KPMG, including extensive time in East Asia, is developing an automated portfolio optimizer. What that means is that a client will be able to see the entire range of what-ifs? along the risk/return spectrum.
Zimmerman intends to fundraise and bring the solution to bank treasurers, sell-side researchers, and relationship managers at private banks.
Traditionally, quant models to work out a portfolio composition ended up with a single number, value at risk, or VAR, which most analysts and PMs relied on (lazily). VAR was a single number, a statistical likelihood of the portfolio losing its money. Overreliance on an easy figure, and ignoring the nuances around it, led to lots of heavy losses during the 2008 crisis.
The R.M. uses this to become a coach instead of a salesperson
Andreas Zimmerman, FinHorizons
Zimmerman’s model incorporates far more asset classes, and then analyzes the interdependencies (the relationships) among them. It’s more sophisticated than a simple covariance analysis (how one asset class impacts another).
“The technology is pure quant,” he said. “Today people call this artificial intelligence.” But he keeps the work tailored to how people work in financial services today: the outcomes can all be downloaded onto Excel.
What differs is that Zimmerman’s tech is meant to be applied to a client’s goals-oriented framework, so the portfolio manager can dial exposures and risk up and down, with every scenario over any time period mapped out. It’s a visual way to understand what, exactly, a client’s getting into with a given portfolio. But it is meaningless if a sell side is just pushing a particular product.
“The relationship manager uses this to become a coach instead of a salesperson,” Zimmerman said.
From his experience working with Asian banks, he believes institutions in Hong Kong and Singapore are more likely to become his first clients. In the meantime he will need to find a partner that can add a nicer front end.
This company is now in its fifth year, selling a solution allowing relationship managers to identify what funds are legally suited to a given client.
Tobias Houdek, product and marketing manager, says the company relies on fund data compiled by its strategic investor, FundInfo. He set up Investment Navigator with the company’s CEO, Alberto Rama, after they worked together at UBS.
Investment Navigator uses some basic A.I. tools to read the legal documents with each fund, and manages a database of what funds can be sold to whom. It’s a database, though, not a self-learning solution.
Its task is more complicated than it sounds, though, because the range of share classes has exploded to the thousands. Banks also have their own rules of who can buy what.
For example, a Brazilian investor may not be allowed to invest in a structured product that uses Brazilian underlying assets – if the customer is based overseas. Any factor – the person’s passport, their residence, the location of their booking center or advisor – can affect whether or not they can buy a product. Sometimes a negative outcome is absolute, and sometimes it’s conditional on other compliance checks.
Most banks’ relationship management teams take 45 minutes of RM time to look up this information. Investment Navigator cuts the process down to one minute, as well as provides alternatives in case a particular product turns out to be a problem.
Houdek says the challenge is how wealth managers integrate the system into their current processes. The company is adding securities and other bells and whistles to round out its software-as-a-subscription offer. The firm also hopes to win its first buy-side clients, with those in Asia likely first movers; it has recently opened a sales office in Singapore.
CEO and founder Oliver Freigang has developed this company to automate calculations of waterfall payouts by private-equity firms and other alternative asset managers.
P.E. firms pursue complicated methods in how they work out how much they should earn, and how much they should pay out to their clients, the investors (or, in industry lingo, the limited partners, or L.P.s).
Currently all firms do this using Excel, but the complexity of these calculations makes this prone to errors and time consuming. It is also very difficult for third parties (clients, auditors, employees) to understand payouts – which has suited private-equity executives just fine.
An auditor has no chance understanding this
Oliver Freigang, Qashqade
Many factors go into how a P.E. firm decides to divvy up its earnings. Usually it first pays back capital contributions from investors, with interest – but at what point they charge interest, on what base, and how they work out the “catch up” (the firm’s own cut) are all kept mysterious. But working these out even internally is a time-consuming process, and only the savviest Excel formula jockeys avoid making mistakes that can throw the numbers off.
“An auditor has no chance of understanding this,” he said.
But in some cases they may wish to share this information; at the very least it would save days’ worth of work with accountants and auditors. Or L.P.s could also use the tool to estimate quickly what payout they should get.
The software has been designed so that non-experts, with a little training, can easily drop’n’drag numbers and create a waterfall quickly, said Freigang. Normally this can take days. Unlike waterfalls on Excel, which mingle the payout calculcations with a log of transactions, Qashqade keeps these workflows separate until the last minute, when the actual calculation is made. That allows users flexibility, so they can look at an entire fund or a single deal.
Freigang notes that alternative asset managers are increasing their portfolio exposures to fintech and other digital-first companies – but remain very old-school in their own firms. Also, fintech requires domain expertise, and hardly anyone in tech understands private equity.